Optimal trajectory generation in joint space for 6R industrial serial robots using cuckoo search algorithm

Karahan, Oğuzhan; Karci, Hasan; Tangel, Ali

doi:10.1007/s11370-022-00440-8

Cited by 10 publications

(7 citation statements)

References 40 publications

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“…Compared to other algorithms, the ACS algorithm had better performance, with a better convergence speed and greater efficiency. Karahan et al [47] presented a novel trajectory generation method that considered time optimization, jerk optimization, and time-jerk optimization. The improved CS was proposed and compared to earlier studies, and the results showed that the improved CS was more effective than other algorithms.…”

Section: Advantagesmentioning

confidence: 99%

Inverse Kinematics of Robot Manipulator Based on BODE-CS Algorithm

Luo

Qiao

2023

Machines

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Differential evolution is a popular algorithm for solving global optimization problems. When tested, it has reportedly outperformed both robotic problems and benchmarks. However, it may have issues with local optima or premature convergence. In this paper, we present a novel BODE-CS (Bidirectional Opposite Differential Evolution–Cuckoo Search) algorithm to solve the inverse kinematics problem of a six-DOF EOD (Explosive Ordnance Disposal) robot manipulator. The hybrid algorithm was based on the differential evolution algorithm and Cuckoo Search algorithm. To avoid any local optimum and accelerate the convergence of the swarm, various strategies were introduced. Firstly, a forward-kinematics model was established, and the objective function was formulated according to the structural characteristics of the robot manipulator. Secondly, a Halton sequence and an opposite search strategy were used to initialize the individuals in the swarm. Thirdly, the optimization algorithms applied to the swarm were dynamically allocated to the Differential Evolution algorithm or the Cuckoo algorithm. Fourthly, a composite differential algorithm, which consisted of a dynamically opposite differential strategy, a bidirectional search strategy, and two other typically used differential strategies were introduced to maintain the diversity of the swarm. Finally, two adaptive parameters were introduced to optimize the amplification factor F and cross-over probability Cr. To verify the performance of the BODE-CS algorithm, two different tasks were tested. The experimental results of the simulation showed that the BODE-CS algorithm had high accuracy and a fast convergence rate, which met the requirements of an inverse solution for the manipulator.

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Section: Advantagesmentioning

confidence: 99%

Inverse Kinematics of Robot Manipulator Based on BODE-CS Algorithm

Luo

Qiao

2023

Machines

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“…[24] GA, DE 6-DOF manipulator Path planning [25] DE 7-DOF manipulator IK solutions [26] CS 6-DOF manipulator Path planning…”

Section: Reference Metaheuristics Robotic System Applicationmentioning

confidence: 99%

A Metaheuristic Optimization Approach to Solve Inverse Kinematics of Mobile Dual-Arm Robots

et al. 2022

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This work presents an approach to solving the inverse kinematics of mobile dual-arm robots based on metaheuristic optimization algorithms. First, a kinematic analysis of a mobile dual-arm robot is presented. Second, an objective function is formulated based on the forward kinematics equations. The kinematic analysis does not require using any Jacobian matrix nor its estimation; for this reason, the proposed approach does not suffer from singularities, which is a common problem with conventional inverse kinematics algorithms. Moreover, the proposed method solves cooperative manipulation tasks, especially in the case of coordinated manipulation. Simulation and real-world experiments were performed to verify the proposal’s effectiveness under coordinated inverse kinematics and trajectory tracking tasks. The experimental setup considered a mobile dual-arm system based on the KUKA® Youbot® robot. The solution of the inverse kinematics showed precise and accurate results. Although the proposed approach focuses on coordinated manipulation, it can be implemented to solve non-coordinated tasks.

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“…Notably, Zhao et al (2022) introduced a hybrid optimization approach, combining the Improved Gray Wolf Optimizer and PSO, for 6‐DOF robot joint space TJOTP. Karahan et al (2022) employed various interpolation functions, including triangular splines, cubic splines, and combinations of cubic splines with seventh‐order polynomials, for joint trajectory interpolation based on the PUMA560 robot. They utilized the Cuckoo Search (CS) algorithm to seek time–jerk optimal trajectory and conducted comparative simulations with existing methods to validate the superiority of CS solutions.…”

Section: Introductionmentioning

confidence: 99%

Time–jerk optimal trajectory planning for industrial robots with coupled interpolation function selection

Wang,

Zhang,

Zhou

et al. 2024

Journal of Field Robotics

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In the contemporary field of optimal trajectory planning for industrial robots, it is customary to construct trajectories through the manual predefinition of interpolation functions. Unfortunately, this method frequently overlooks the influence of the interpolation function itself on the optimization objectives, resulting in suboptimal outcomes. To remedy this limitation, an optimal trajectory planning method with coupled interpolation function selection is proposed, in which the total task time and the integral squared jerk are defined as optimization objectives. This method minimizes the optimization objectives while also factoring in the optimal interpolation function, and avoiding subjective interference. To address the aforementioned biobjective optimization problem better, an Improved MultiObjective Golden Eagle Optimizer is introduced. Population diversity and the ability to escape local optima are enhanced through the incorporation of Chaotic Mapping, Opposition‐Based Learning, Differential Evolution, and adaptive inertia weight strategy into the algorithm. The superiority of the algorithm is validated through a series of simulations on 17 benchmark functions. In the context of the robotic stirring operation within the automated block cast charging process, the proposed method is utilized to derive the time–jerk optimal trajectory. The results demonstrate the effectiveness of the proposed method.

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Optimal trajectory generation in joint space for 6R industrial serial robots using cuckoo search algorithm

Cited by 10 publications

References 40 publications

Inverse Kinematics of Robot Manipulator Based on BODE-CS Algorithm

Inverse Kinematics of Robot Manipulator Based on BODE-CS Algorithm

A Metaheuristic Optimization Approach to Solve Inverse Kinematics of Mobile Dual-Arm Robots

Time–jerk optimal trajectory planning for industrial robots with coupled interpolation function selection

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